This SBIR project is developing the first geostatistical software to offer tools that are specifically designed for the analysis of health disparities, providing: descriptions of spatial patterns of cancer mortality rates and identification of scales of variability, spatial filtering to correct for statistical instability caused by the smaller size of minority populations, statistical tests to detect significant differences in cancer risks among sub-populations, detection of clusters and outliers of significantly high or low health disparities, exploration of local relationships with covariates (i.e. demography, behavioral or socio-economic variables) using geographically-weighted regression and multi-level analysis, and visualization of changes in disparities through time. This project will [unreadable] accomplish 6 aims: 1. Conduct a requirements analysis to identify the spatial methods and functionality to incorporate into the software. 2. Develop innovative geostatistical techniques for spatial filtering of cancer rates and statistical tests to detect significant differences in cancer rates among sub-populations. 3. Develop new methodologies for rate filtering over small geographies, incorporation of spatial interactions among neighborhoods into multi-level analysis, assessment and propagation of the uncertainty attached to filtered rates through the statistical analysis. 4. Build and test a complete set of functionalities based on the research results and simulation studies, and incorporate them into Biomedware's space-time visualization and analysis technology. 5. Apply the software and methods to demonstrate the approach and its unique benefits for the measurement, mapping, detection and explanation of health disparities. 6. Create instructional materials, including a short course, to foster the adoption of this approach in health science. Feasibility of this project was demonstrated in Phase I. This Phase II project will accomplish aims three through six. These technologic, scientific and commercial innovations will revolutionize our ability to interprete geographic variation in cancer disparities, detect changes in space (e.g. cluster or anomalies) or through time (e.g. change in health disparities following strategies to improve cancer prevention and early detection), and to better understand the causes underlying observed racial disparities in cancer incidence, mortality and morbidity. [unreadable] [unreadable] [unreadable] [unreadable]